Envestnet®| Yodlee®'s Bank Statement Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

Dataset

This dataset was created by Chelangat sharon

Released under Apache 2.0

Contents

In recent years, we have been exploring computational models to classify bank accounts in combating illegal pyramid selling. The department of economic investigation provides us with plenty of transaction data of real bank accounts. An account contains a lot of transaction records, each of which includes bilateral transaction accounts, timestamp, amount of money and transaction direction, etc. We sample out the transaction records belonging to 10145 bank accounts to form out dataset for training our model. There are 9270 normal accounts and 875 accounts involving a MLM organization respectively. The number of transaction records generated by the normal accounts run up to 6732730 and the fraud records created by MLM members amount to 275804 rows. These MLM members are manually annotated as ``illegal'' by economic investigators. Before training the models, we filtered out some noisy data, i.e. deleting the duplicate records, incomplete records and the records whose transaction amounts no more than 50. Therefore, 1371914 records is filtered out from the set of normal accounts' transaction records and 91341 records created by illegal accounts are deleted. In general, more than 5 million transaction records are used after denoising.

Dataset Description This dataset contains details of various bank transactions. The data includes both debit and credit transactions made using different modes such as card, ATM, and UPI. Each transaction record provides comprehensive information, including the type of transaction, the mode of payment, the amount transacted, the current balance after the transaction, timestamps, and additional details such as narration and transaction ID.

Columns type: The type of transaction (DEBIT or CREDIT). mode: The mode of the transaction (e.g., CARD, ATM, UPI, OTHERS). amount: The amount involved in the transaction. currentBalance: The account balance after the transaction. transactionTimestamp: The timestamp of when the transaction occurred. valueDate: The date the transaction is valued. txnId: A unique identifier for the transaction. narration: A brief description of the transaction. reference: Additional reference information, if any.

Usage This dataset can be used for various analytical purposes, including but not limited to:

• Analyzing spending patterns.
• Studying the frequency and types of transactions.
• Developing predictive models for transaction behaviors.
• Financial and economic research.

Source The dataset is a synthetic creation for educational and analytical purposes. It provides a realistic representation of transaction data typically found in bank statements. It is generated using real data

Big Data Analytics in Banking Market Outlook

The Big Data Analytics in Banking market size was valued at approximately USD 23.5 billion in 2023, and it is projected to grow to USD 67.2 billion by 2032, showcasing a robust CAGR of 12.3%. This exponential growth is driven by the increasing demand for more refined data analysis tools that enable banks to manage vast amounts of information and derive actionable insights. The banking sector is increasingly acknowledging the need for advanced analytics to enhance decision-making processes, improve customer satisfaction, and mitigate risks. Factors such as digital transformation, regulatory pressure, and the need for operational efficiency continue to propel the market forward.

One of the primary growth factors in the Big Data Analytics in Banking market is the heightened emphasis on risk management. Banks are continuously exposed to various risks, including credit, market, operational, and liquidity risks. Big Data Analytics plays a crucial role in identifying, measuring, and mitigating these risks. By analyzing large volumes of structured and unstructured data, banks can gain insights into potential risk factors and develop strategies to address them proactively. Furthermore, regulatory requirements mandating more stringent risk management practices have compelled banks to invest in sophisticated analytics solutions, further contributing to market growth.

Another significant driver of this market is the increasing need for enhanced customer analytics. With the rise of digital banking and fintech solutions, customers now demand more personalized services and experiences. Big Data Analytics enables banks to understand customer behavior, preferences, and needs by analyzing transaction histories, social media interactions, and other data sources. By leveraging these insights, banks can offer tailored products and services, improve customer retention rates, and gain a competitive edge in the market. Additionally, customer analytics helps banks identify cross-selling and up-selling opportunities, thereby driving revenue growth.

Fraud detection is also a critical area where Big Data Analytics has made a significant impact in the banking sector. The increasing complexity and frequency of financial frauds necessitate the adoption of advanced analytics solutions to detect and prevent fraudulent activities effectively. Big Data Analytics allows banks to analyze vast amounts of transaction data in real-time, identify anomalies, and flag suspicious activities. By employing machine learning algorithms, banks can continuously improve their fraud detection capabilities, minimizing financial losses and enhancing security for their customers. This ongoing investment in fraud detection tools is expected to contribute significantly to the growth of the Big Data Analytics in Banking market.

Data Analytics In Financial services is revolutionizing the way banks operate by providing deeper insights into financial trends and customer behaviors. This transformative approach enables financial institutions to analyze vast datasets, uncovering patterns and correlations that were previously inaccessible. By leveraging data analytics, banks can enhance their financial forecasting, optimize asset management, and improve investment strategies. The integration of data analytics in financial operations not only aids in risk assessment but also supports regulatory compliance by ensuring accurate and timely reporting. As the financial sector continues to evolve, the role of data analytics becomes increasingly pivotal in driving innovation and maintaining competitive advantage.

Regionally, North America remains a dominant player in the Big Data Analytics in Banking market, driven by the presence of major banking institutions and technology firms. The region's early adoption of advanced technologies and a strong focus on regulatory compliance have been pivotal in driving market growth. Europe follows closely, with stringent regulatory frameworks like GDPR necessitating advanced data management and analytics solutions. In the Asia Pacific region, rapid digital transformation and the growing adoption of mobile banking are key factors propelling the market forward. The Middle East & Africa and Latin America, while currently smaller markets, are experiencing steady growth as banks in these regions increasingly invest in analytics solutions to enhance their competitive positioning.

Component Analysis

In the Big Data Analytics in

The balance of payments is a record of a country's international transactions with the rest of the world. It is composed of the current account and the capital and financial account. The current account is itself subdivided into goods, services, income and current transfers; it registers the value of exports (credits) and imports (debits). The difference between these two values is the "balance".

The dataset contains year- and month-wise compiled data from the year 2020 to till date on the number of digital payment transactions done by location and through through the systems such as Aadhar Enabled Payment System (AEPS), Cheque Truncation System (CTS), National Automated Clearing System (NACH), National Electronic Toll Collection (NETC), Real Time Gross Settlement (RTGS), Immediate Payment Service (IMPS), National Electronic Fund Transactions (NEFT) and other modes of payment

Notes: i) The data published is only for RBI, NPCI-operated systems and Card Networks (domestic Off-Us transactions). ii) RTGS data includes only Customer and Interbank transactions. iii) AePS data under Payment transactions include AePS Fund Transfers and BHIM Aadhaar Pay transactions. iv) UPI data includes BHIM-UPI and USSD transactions. v) NACH Credit data includes Aadhaar Payment Bridge System (APBS) transactions. vi) NETC figures are for FASTags linked with all instruments and hence would be different from the monthly bulletin which only includes NETC linked to bank accounts vii) BBPS data is not included in the monthly bulletin as the data is captured under other systems. viii) Data on Prepaid cards are only those that are processed by card networks. ix) Blanks in the dataset represent holiday

India Mobile Banking Transactions: Value data was reported at 37,696,017.240 INR mn in Mar 2025. This records an increase from the previous number of 32,155,172.112 INR mn for Feb 2025. India Mobile Banking Transactions: Value data is updated monthly, averaging 1,798,543.365 INR mn from Apr 2011 (Median) to Mar 2025, with 168 observations. The data reached an all-time high of 37,696,017.240 INR mn in Mar 2025 and a record low of 760.000 INR mn in Apr 2011. India Mobile Banking Transactions: Value data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Monetary – Table IN.KAI017: Mobile Payments. [COVID-19-IMPACT]

India Card Payments Transactions: Debit Card: Others: Volume: Public Sector Banks: UCO Bank data was reported at 827.000 Unit in Mar 2025. This records an increase from the previous number of 672.000 Unit for Feb 2025. India Card Payments Transactions: Debit Card: Others: Volume: Public Sector Banks: UCO Bank data is updated monthly, averaging 874.000 Unit from Mar 2022 (Median) to Mar 2025, with 37 observations. The data reached an all-time high of 1,518.000 Unit in Jun 2022 and a record low of 494.000 Unit in Nov 2024. India Card Payments Transactions: Debit Card: Others: Volume: Public Sector Banks: UCO Bank data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Monetary – Table IN.KAI014: Card Payments Transactions: Debit Cards: by Bankwise.

Serbia Banks Deposits: Short Term: Transaction data was reported at 465,080.890 RSD mn in Jun 2018. This records a decrease from the previous number of 468,714.700 RSD mn for May 2018. Serbia Banks Deposits: Short Term: Transaction data is updated monthly, averaging 140,084.000 RSD mn from Aug 2001 (Median) to Jun 2018, with 203 observations. The data reached an all-time high of 468,714.700 RSD mn in May 2018 and a record low of 27,519.000 RSD mn in Aug 2001. Serbia Banks Deposits: Short Term: Transaction data remains active status in CEIC and is reported by National Bank of Serbia. The data is categorized under Global Database’s Serbia – Table RS.KB001: Banks Deposits.

Greetings , fellow analysts !

(NOTE : This is a random dataset generated using python. It bears no resemblance to any real entity in the corporate world. Any resemblance is a matter of coincidence.)

REC-SSEC Bank is a govt-aided bank operating in the Indian Peninsula. They have regional branches in over 40+ regions of the country. You have been provided with a massive excel sheet containing the transaction details, the total transaction amount and their location and total transaction count.

The dataset is described as follows :

1. Date - The date on which the transaction took place. 2.Domain - Where or which type of Business entity made the transaction. 3.Location - Where the data is collected from 4.Value - Total value of transaction
2. Count of transaction .

For example , in the very first row , the data can be read as : " On the first of January, 2022 , 1932 transactions of summing upto INR 365554 from Bhuj were reported " NOTE : There are about 2750 transactions every single day. All of this has been given to you.

The bank wants you to answer the following questions :

1. What is the average transaction value everyday for each domain over the year.
2. What is the average transaction value for every city/location over the year
3. The bank CEO , Mr: Hariharan , wants to promote the ease of transaction for the highest active domain. If the domains could be sorted into a priority, what would be the priority list ?
4. What's the average transaction count for each city ?

Bank Statement Analyzer Market Outlook

The global bank statement analyzer market size was valued at USD 1.2 billion in 2023 and is projected to reach USD 3.8 billion by 2032, growing at a CAGR of 12.5% during the forecast period. The growth of the bank statement analyzer market is driven by the increasing need for automated financial analysis, which provides more accurate and quicker insights into financial transactions, enhancing the efficiency and decision-making processes for various financial institutions.

One of the primary growth factors fueling the bank statement analyzer market is the rapid technological advancement in artificial intelligence (AI) and machine learning (ML). These technologies enable more sophisticated data analysis, allowing bank statement analyzers to provide detailed and precise financial insights. Financial institutions are increasingly relying on these tools to reduce manual errors, mitigate risks, and streamline operations, thus driving the market's expansion. Additionally, the growing adoption of digital banking services has necessitated the use of advanced tools to manage the vast amounts of transaction data generated, further propelling the demand for bank statement analyzers.

Another significant growth driver is the rising regulatory compliance requirements across the globe. Financial institutions are under constant scrutiny to ensure adherence to stringent regulations and reporting standards. Bank statement analyzers help in maintaining compliance by providing accurate transaction records and facilitating easy audits and reports. This not only reduces the risk of non-compliance but also saves time and resources, making these analyzers an essential tool for banks and financial institutions.

The increasing demand for personalized banking solutions also contributes to the growth of the bank statement analyzer market. Customers today expect personalized services that cater to their specific financial needs and preferences. Bank statement analyzers leverage AI and ML to analyze customer transaction data and derive insights that can be used to offer tailored financial products and services. This enhances customer satisfaction and loyalty, driving banks and financial institutions to adopt these advanced analysis tools.

The integration of Smart Finance Technologies is revolutionizing the way financial institutions handle their data. These technologies encompass a range of digital tools and platforms designed to enhance financial management and analysis. By leveraging smart finance solutions, banks and financial institutions can automate complex tasks, improve accuracy, and reduce the time spent on manual processes. This not only boosts operational efficiency but also enables institutions to provide better services to their clients. As the demand for more intelligent and adaptive financial solutions grows, the role of Smart Finance Technologies becomes increasingly pivotal in shaping the future of banking and finance.

From a regional perspective, North America holds a significant share of the global bank statement analyzer market due to the early adoption of advanced technologies and the presence of major financial institutions. The region's strong regulatory framework and high focus on digital transformation in the banking sector further support market growth. Europe also offers substantial growth opportunities with its stringent compliance requirements and increasing investment in financial technology. Meanwhile, the Asia Pacific region is expected to witness rapid growth due to the rising number of digital banking users and increased adoption of AI-based financial tools.

Component Analysis

In the bank statement analyzer market, the component segment is divided into software and services. The software segment encompasses various platforms and applications that automate the analysis of bank statements. These software solutions utilize advanced algorithms and machine learning techniques to provide accurate insights and predictions. The increasing demand for efficient and effective financial analysis tools has led to significant advancements in bank statement analyzer software, contributing to the market's growth. Furthermore, the integration of AI and ML in these software solutions enables more accurate and quicker data processing, which is crucial for financial institutions to make informed decisions.

The services segment includes consulting, implemen

Serbia Banks Deposits: Annual: Short Term: Transaction data was reported at 458,493.950 RSD mn in 2017. This records an increase from the previous number of 409,314.200 RSD mn for 2016. Serbia Banks Deposits: Annual: Short Term: Transaction data is updated yearly, averaging 139,820.000 RSD mn from Dec 1997 (Median) to 2017, with 21 observations. The data reached an all-time high of 458,493.950 RSD mn in 2017 and a record low of 3,883.000 RSD mn in 1997. Serbia Banks Deposits: Annual: Short Term: Transaction data remains active status in CEIC and is reported by National Bank of Serbia. The data is categorized under Global Database’s Serbia – Table RS.KB001: Banks Deposits.

ANZ Banking data. It contains financial transaction data of ANZ Bank.

Financial Transaction data from ANZ bank program on Forage.

Data contains financial transactions and sample banking data.

ANZ Banking data. It contains financial transaction data of ANZ Bank.

Financial Transaction data from ANZ bank program on Forage.

Data contains financial transactions and sample banking data.

Envestnet®| Yodlee®'s Travel Transaction Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

Graph and download economic data for Rest of the World; Interbank Transactions with Banks in Foreign Countries; Asset, Level (ROWNIBA027N) from 1945 to 2024 about IMA, interbank, foreign, transactions, Net, assets, banks, and depository institutions.

Graph and download economic data for International Banking Facilities of U.S.-Chartered Depository Institutions; Total Assets (Call Report), Transactions (BOGZ1FA274090273Q) from Q4 1946 to Q4 2024 about U.S.-chartered, transactions, assets, banks, depository institutions, and USA.

Characteristics of the dataset.

Dataset

This dataset was created by Harshita Dubey

Released under CC0: Public Domain

Contents